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Christos Tachtatzis

Bio: Christos Tachtatzis is an academic researcher from University of Strathclyde. The author has contributed to research in topics: Intrusion detection system & Wireless sensor network. The author has an hindex of 20, co-authored 115 publications receiving 1587 citations. Previous affiliations of Christos Tachtatzis include Czech Technical University in Prague & Letterkenny Institute of Technology.


Papers
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Proceedings ArticleDOI
14 May 2016
TL;DR: In this article, a multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks.
Abstract: The Internet of things (IoT) is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks.

348 citations

Posted Content
TL;DR: A taxonomy and survey of shallow and deep networks intrusion detection systems is presented based on previous and current works, and a discussion of the false and true positive alarm rates is presented to help researchers model reliable and efficient machine learning based intrusion Detection systems.
Abstract: Intrusion detection has attracted a considerable interest from researchers and industries. The community, after many years of research, still faces the problem of building reliable and efficient IDS that are capable of handling large quantities of data, with changing patterns in real time situations. The work presented in this manuscript classifies intrusion detection systems (IDS). Moreover, a taxonomy and survey of shallow and deep networks intrusion detection systems is presented based on previous and current works. This taxonomy and survey reviews machine learning techniques and their performance in detecting anomalies. Feature selection which influences the effectiveness of machine learning (ML) IDS is discussed to explain the role of feature selection in the classification and training phase of ML IDS. Finally, a discussion of the false and true positive alarm rates is presented to help researchers model reliable and efficient machine learning based intrusion detection systems.

190 citations

Journal ArticleDOI
TL;DR: In this paper, the authors provide researchers with two key pieces of information; a survey of prominent datasets, analyzing their use and impact on the development of the past decade's Intrusion Detection Systems (IDS) and a taxonomy of network threats and associated tools to carry out these attacks.
Abstract: As the world moves towards being increasingly dependent on computers and automation, building secure applications, systems and networks are some of the main challenges faced in the current decade. The number of threats that individuals and businesses face is rising exponentially due to the increasing complexity of networks and services of modern networks. To alleviate the impact of these threats, researchers have proposed numerous solutions for anomaly detection; however, current tools often fail to adapt to ever-changing architectures, associated threats and zero-day attacks. This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems (NIDS) and the growing number of sophisticated threats. To this end, this manuscript provides researchers with two key pieces of information; a survey of prominent datasets, analyzing their use and impact on the development of the past decade’s Intrusion Detection Systems (IDS) and a taxonomy of network threats and associated tools to carry out these attacks. The manuscript highlights that current IDS research covers only 33.3% of our threat taxonomy. Current datasets demonstrate a clear lack of real-network threats, attack representation and include a large number of deprecated threats, which together limit the detection accuracy of current machine learning IDS approaches. The unique combination of the taxonomy and the analysis of the datasets provided in this manuscript aims to improve the creation of datasets and the collection of real-world data. As a result, this will improve the efficiency of the next generation IDS and reflect network threats more accurately within new datasets.

114 citations

Posted Content
09 Jun 2018
TL;DR: This manuscript aims to provide researchers with a taxonomy and survey of current dataset composition and current Intrusion Detection Systems (IDS) capabilities and assets to improve both the efficiency of IDS and the creation of datasets to build the next generation IDS as well as to reflect networks threats more accurately in future datasets.
Abstract: With the world moving towards being increasingly dependent on computers and automation, one of the main challenges in the current decade has been to build secure applications, systems and networks. Alongside these challenges, the number of threats is rising exponentially due to the attack surface increasing through numerous interfaces offered for each service. To alleviate the impact of these threats, researchers have proposed numerous solutions; however, current tools often fail to adapt to ever-changing architectures, associated threats and 0-days. This manuscript aims to provide researchers with a taxonomy and survey of current dataset composition and current Intrusion Detection Systems (IDS) capabilities and assets. These taxonomies and surveys aim to improve both the efficiency of IDS and the creation of datasets to build the next generation IDS as well as to reflect networks threats more accurately in future datasets. To this end, this manuscript also provides a taxonomy and survey or network threats and associated tools. The manuscript highlights that current IDS only cover 25% of our threat taxonomy, while current datasets demonstrate clear lack of real-network threats and attack representation, but rather include a large number of deprecated threats, hence limiting the accuracy of current machine learning IDS. Moreover, the taxonomies are open-sourced to allow public contributions through a Github repository.

93 citations

Proceedings ArticleDOI
01 Jan 2010
TL;DR: The analysis determines the maximum device lifetime for a range of scheduled allocations and shows that the higher the data rate of frame transfers the longer the device lifetime.
Abstract: Body Area Networks (BANs) are an emerging area of wireless personal communications. The IEEE 802.15.6 working group aims to develop a communications standard optimised for low power devices operating on, in or around the human body. IEEE 802.15.6 specifically targets low power medical application areas. The IEEE 802.15.6 draft defines two main channel access modes; contention based and contention free. This paper examines the energy lifetime performance of contention free access and in particular of periodic scheduled allocations. This paper presents an overview of the IEEE 802.15.6 and an analytical model for estimating the device lifetime. The analysis determines the maximum device lifetime for a range of scheduled allocations. It also shows that the higher the data rate of frame transfers the longer the device lifetime. Finally, the energy savings provided by block transfers are quantified and compared to immediately acknowledged alternatives.

92 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Reference EntryDOI
31 Oct 2001
TL;DR: The American Society for Testing and Materials (ASTM) as mentioned in this paper is an independent organization devoted to the development of standards for testing and materials, and is a member of IEEE 802.11.
Abstract: The American Society for Testing and Materials (ASTM) is an independent organization devoted to the development of standards.

3,792 citations

Journal ArticleDOI
TL;DR: The current state-of-art of WBANs is surveyed based on the latest standards and publications, and open issues and challenges within each area are explored as a source of inspiration towards future developments inWBANs.
Abstract: Recent developments and technological advancements in wireless communication, MicroElectroMechanical Systems (MEMS) technology and integrated circuits has enabled low-power, intelligent, miniaturized, invasive/non-invasive micro and nano-technology sensor nodes strategically placed in or around the human body to be used in various applications, such as personal health monitoring. This exciting new area of research is called Wireless Body Area Networks (WBANs) and leverages the emerging IEEE 802.15.6 and IEEE 802.15.4j standards, specifically standardized for medical WBANs. The aim of WBANs is to simplify and improve speed, accuracy, and reliability of communication of sensors/actuators within, on, and in the immediate proximity of a human body. The vast scope of challenges associated with WBANs has led to numerous publications. In this paper, we survey the current state-of-art of WBANs based on the latest standards and publications. Open issues and challenges within each area are also explored as a source of inspiration towards future developments in WBANs.

1,359 citations

Journal ArticleDOI
23 Jan 2018
TL;DR: This paper presents a novel deep learning technique for intrusion detection, which addresses concerns regarding the feasibility and sustainability of current approaches when faced with the demands of modern networks and details the proposed nonsymmetric deep autoencoder (NDAE) for unsupervised feature learning.
Abstract: Network intrusion detection systems (NIDSs) play a crucial role in defending computer networks. However, there are concerns regarding the feasibility and sustainability of current approaches when faced with the demands of modern networks. More specifically, these concerns relate to the increasing levels of required human interaction and the decreasing levels of detection accuracy. This paper presents a novel deep learning technique for intrusion detection, which addresses these concerns. We detail our proposed nonsymmetric deep autoencoder (NDAE) for unsupervised feature learning. Furthermore, we also propose our novel deep learning classification model constructed using stacked NDAEs. Our proposed classifier has been implemented in graphics processing unit (GPU)-enabled TensorFlow and evaluated using the benchmark KDD Cup ’99 and NSL-KDD datasets. Promising results have been obtained from our model thus far, demonstrating improvements over existing approaches and the strong potential for use in modern NIDSs.

979 citations